N
Neil Shephard
Researcher at Harvard University
Publications - 219
Citations - 32524
Neil Shephard is an academic researcher from Harvard University. The author has contributed to research in topics: Stochastic volatility & Volatility (finance). The author has an hindex of 68, co-authored 219 publications receiving 30586 citations. Previous affiliations of Neil Shephard include University of Oxford & London School of Economics and Political Science.
Papers
More filters
Posted Content
Analysis of High Dimensional Multivariate Stochastic Volatility Models
TL;DR: In this article, the authors consider the fitting and comparison of high-dimensional multivariate time series models with time varying correlations, and propose an estimation, filtering and model choice algorithm.
Posted Content
Panel Experiments and Dynamic Causal Effects: A Finite Population Perspective
TL;DR: In this article, a nonparametric estimator that is unbiased over the randomization distribution and derive its finite population limiting distribution as either the sample size or the duration of the experiment increases is presented.
Journal ArticleDOI
Integer‐valued Trawl Processes: A Class of Stationary Infinitely Divisible Processes
TL;DR: In this article, the authors introduce a new continuous-time framework for modelling serially correlated count and integer-valued data and apply it to high-frequency financial data, where the key component in their new model is the class of integervalued trawl processes, which are serially correlation, stationary, infinitely divisible processes.
Posted Content
Estimation and Testing of Stochastic Variance Models
Andrew Harvey,Neil Shephard +1 more
TL;DR: In this paper, a stochastic variance model is estimated by quasi-maximum likelihood procedure by transforming to a linear state space form and the properties of observations corrected for heteroscedasticity can be derived.
Posted Content
Nuisance parameters, composite likelihoods and a panel of garch models
TL;DR: In this article, the authors investigate the properties of the composite likelihood (CL) method for (T × NT ) GARCH panels and show that when T is reasonably large, CL performs well.